task_path stringlengths 3 199 ⌀ | dataset stringlengths 1 128 ⌀ | model_name stringlengths 1 223 ⌀ | paper_url stringlengths 21 601 ⌀ | metric_name stringlengths 1 50 ⌀ | metric_value stringlengths 1 9.22k ⌀ |
|---|---|---|---|---|---|
Medical Image Segmentation | Kvasir-SEG | DuAT | https://arxiv.org/abs/2212.11677v1 | mean Dice | 0.924 |
Medical Image Segmentation | Kvasir-SEG | DuAT | https://arxiv.org/abs/2212.11677v1 | mIoU | 0.876 |
Medical Image Segmentation | Kvasir-SEG | MSRF-Net | https://arxiv.org/abs/2105.07451v2 | mean Dice | 0.9217 |
Medical Image Segmentation | Kvasir-SEG | MSRF-Net | https://arxiv.org/abs/2105.07451v2 | mIoU | 0.8914 |
Medical Image Segmentation | Kvasir-SEG | ADSNet | https://arxiv.org/abs/2405.07523v1 | mean Dice | 0.92 |
Medical Image Segmentation | Kvasir-SEG | ADSNet | https://arxiv.org/abs/2405.07523v1 | mIoU | 0.871 |
Medical Image Segmentation | Kvasir-SEG | CaraNet | https://arxiv.org/abs/2108.07368v3 | Average MAE | 0.023 |
Medical Image Segmentation | Kvasir-SEG | CaraNet | https://arxiv.org/abs/2108.07368v3 | mean Dice | 0.918 |
Medical Image Segmentation | Kvasir-SEG | CaraNet | https://arxiv.org/abs/2108.07368v3 | S-Measure | 0.929 |
Medical Image Segmentation | Kvasir-SEG | CaraNet | https://arxiv.org/abs/2108.07368v3 | max E-Measure | 0.968 |
Medical Image Segmentation | Kvasir-SEG | CaraNet | https://arxiv.org/abs/2108.07368v3 | mIoU | 0.865 |
Medical Image Segmentation | Kvasir-SEG | TransFuse-L | https://arxiv.org/abs/2102.08005v2 | mean Dice | 0.918 |
Medical Image Segmentation | Kvasir-SEG | TransFuse-L | https://arxiv.org/abs/2102.08005v2 | mIoU | 0.868 |
Medical Image Segmentation | Kvasir-SEG | TransFuse-S | https://arxiv.org/abs/2102.08005v2 | mean Dice | 0.918 |
Medical Image Segmentation | Kvasir-SEG | TransFuse-S | https://arxiv.org/abs/2102.08005v2 | mIoU | 0.868 |
Medical Image Segmentation | Kvasir-SEG | BDG-Net | https://arxiv.org/abs/2201.00767v2 | Average MAE | 0.021 |
Medical Image Segmentation | Kvasir-SEG | BDG-Net | https://arxiv.org/abs/2201.00767v2 | mean Dice | 0.915 |
Medical Image Segmentation | Kvasir-SEG | BDG-Net | https://arxiv.org/abs/2201.00767v2 | S-Measure | 0.923 |
Medical Image Segmentation | Kvasir-SEG | BDG-Net | https://arxiv.org/abs/2201.00767v2 | max E-Measure | 0.972 |
Medical Image Segmentation | Kvasir-SEG | BDG-Net | https://arxiv.org/abs/2201.00767v2 | mIoU | 0.865 |
Medical Image Segmentation | Kvasir-SEG | SAM-EG | https://arxiv.org/abs/2406.14819v1 | mean Dice | 0.915 |
Medical Image Segmentation | Kvasir-SEG | SAM-EG | https://arxiv.org/abs/2406.14819v1 | mIoU | 0.862 |
Medical Image Segmentation | Kvasir-SEG | UniNet | https://pangdatangtt.github.io/#:~:text=guided%20anomaly%20discrimination.-,Abstract,-Anomaly%20detection%20(AD | mean Dice | 0.915 |
Medical Image Segmentation | Kvasir-SEG | UniNet | https://pangdatangtt.github.io/#:~:text=guided%20anomaly%20discrimination.-,Abstract,-Anomaly%20detection%20(AD | mIoU | 0.857 |
Medical Image Segmentation | Kvasir-SEG | MEGANet(Res2Net-50) | https://arxiv.org/abs/2309.03329v3 | Average MAE | 0.025 |
Medical Image Segmentation | Kvasir-SEG | MEGANet(Res2Net-50) | https://arxiv.org/abs/2309.03329v3 | mean Dice | 0.913 |
Medical Image Segmentation | Kvasir-SEG | MEGANet(Res2Net-50) | https://arxiv.org/abs/2309.03329v3 | mIoU | 0.863 |
Medical Image Segmentation | Kvasir-SEG | KDAS | https://arxiv.org/abs/2312.08555v3 | Average MAE | 0.027 |
Medical Image Segmentation | Kvasir-SEG | KDAS | https://arxiv.org/abs/2312.08555v3 | mean Dice | 0.913 |
Medical Image Segmentation | Kvasir-SEG | KDAS | https://arxiv.org/abs/2312.08555v3 | mIoU | 0.848 |
Medical Image Segmentation | Kvasir-SEG | HarDNet-MSEG | https://arxiv.org/abs/2101.07172v2 | Average MAE | 0.025 |
Medical Image Segmentation | Kvasir-SEG | HarDNet-MSEG | https://arxiv.org/abs/2101.07172v2 | mean Dice | 0.912 |
Medical Image Segmentation | Kvasir-SEG | HarDNet-MSEG | https://arxiv.org/abs/2101.07172v2 | S-Measure | 0.923 |
Medical Image Segmentation | Kvasir-SEG | HarDNet-MSEG | https://arxiv.org/abs/2101.07172v2 | max E-Measure | 0.958 |
Medical Image Segmentation | Kvasir-SEG | HarDNet-MSEG | https://arxiv.org/abs/2101.07172v2 | mIoU | 0.857 |
Medical Image Segmentation | Kvasir-SEG | HarDNet-MSEG | https://arxiv.org/abs/2101.07172v2 | FPS | 116 |
Medical Image Segmentation | Kvasir-SEG | UACANet-L | https://arxiv.org/abs/2107.02368v3 | Average MAE | 0.025 |
Medical Image Segmentation | Kvasir-SEG | UACANet-L | https://arxiv.org/abs/2107.02368v3 | mean Dice | 0.912 |
Medical Image Segmentation | Kvasir-SEG | UACANet-L | https://arxiv.org/abs/2107.02368v3 | S-Measure | 0.917 |
Medical Image Segmentation | Kvasir-SEG | UACANet-L | https://arxiv.org/abs/2107.02368v3 | max E-Measure | 0.958 |
Medical Image Segmentation | Kvasir-SEG | UACANet-L | https://arxiv.org/abs/2107.02368v3 | mIoU | 0.862 |
Medical Image Segmentation | Kvasir-SEG | MEGANet(ResNet-34) | https://arxiv.org/abs/2309.03329v3 | Average MAE | 0.026 |
Medical Image Segmentation | Kvasir-SEG | MEGANet(ResNet-34) | https://arxiv.org/abs/2309.03329v3 | mean Dice | 0.911 |
Medical Image Segmentation | Kvasir-SEG | MEGANet(ResNet-34) | https://arxiv.org/abs/2309.03329v3 | mIoU | 0.859 |
Medical Image Segmentation | Kvasir-SEG | ProMISe | https://arxiv.org/abs/2403.04164v3 | mean Dice | 0.911 |
Medical Image Segmentation | Kvasir-SEG | ProMISe | https://arxiv.org/abs/2403.04164v3 | mIoU | 0.851 |
Medical Image Segmentation | Kvasir-SEG | A-DenseUNet | https://www.mdpi.com/1424-8220/21/4/1441 | mean Dice | 0.9085 |
Medical Image Segmentation | Kvasir-SEG | A-DenseUNet | https://www.mdpi.com/1424-8220/21/4/1441 | mIoU | 0.8615 |
Medical Image Segmentation | Kvasir-SEG | UACANet-S | https://arxiv.org/abs/2107.02368v3 | Average MAE | 0.026 |
Medical Image Segmentation | Kvasir-SEG | UACANet-S | https://arxiv.org/abs/2107.02368v3 | mean Dice | 0.905 |
Medical Image Segmentation | Kvasir-SEG | UACANet-S | https://arxiv.org/abs/2107.02368v3 | S-Measure | 0.914 |
Medical Image Segmentation | Kvasir-SEG | UACANet-S | https://arxiv.org/abs/2107.02368v3 | max E-Measure | 0.951 |
Medical Image Segmentation | Kvasir-SEG | UACANet-S | https://arxiv.org/abs/2107.02368v3 | mIoU | 0.852 |
Medical Image Segmentation | Kvasir-SEG | COMMA (ResNet-50) | https://www.mdpi.com/2076-3417/12/4/2114 | Average MAE | 0.024 |
Medical Image Segmentation | Kvasir-SEG | COMMA (ResNet-50) | https://www.mdpi.com/2076-3417/12/4/2114 | mean Dice | 0.904 |
Medical Image Segmentation | Kvasir-SEG | COMMA (ResNet-50) | https://www.mdpi.com/2076-3417/12/4/2114 | S-Measure | 0.925 |
Medical Image Segmentation | Kvasir-SEG | COMMA (ResNet-50) | https://www.mdpi.com/2076-3417/12/4/2114 | max E-Measure | 0.963 |
Medical Image Segmentation | Kvasir-SEG | COMMA (ResNet-50) | https://www.mdpi.com/2076-3417/12/4/2114 | mIoU | 0.860 |
Medical Image Segmentation | Kvasir-SEG | Polyp-SAM++ | https://arxiv.org/abs/2308.06623v1 | mean Dice | 0.902 |
Medical Image Segmentation | Kvasir-SEG | Polyp-SAM++ | https://arxiv.org/abs/2308.06623v1 | mIoU | 0.862 |
Medical Image Segmentation | Kvasir-SEG | Polyp-SAM++ | https://arxiv.org/abs/2308.06623v1 | F-measure | 0.92 |
Medical Image Segmentation | Kvasir-SEG | AG-CUResNeSt | https://arxiv.org/abs/2105.00402v3 | mean Dice | 0.902 |
Medical Image Segmentation | Kvasir-SEG | AG-CUResNeSt | https://arxiv.org/abs/2105.00402v3 | mIoU | 0.845 |
Medical Image Segmentation | Kvasir-SEG | COMMA (Res2Net-50) | https://www.mdpi.com/2076-3417/12/4/2114 | Average MAE | 0.027 |
Medical Image Segmentation | Kvasir-SEG | COMMA (Res2Net-50) | https://www.mdpi.com/2076-3417/12/4/2114 | mean Dice | 0.901 |
Medical Image Segmentation | Kvasir-SEG | COMMA (Res2Net-50) | https://www.mdpi.com/2076-3417/12/4/2114 | S-Measure | 0.919 |
Medical Image Segmentation | Kvasir-SEG | COMMA (Res2Net-50) | https://www.mdpi.com/2076-3417/12/4/2114 | max E-Measure | 0.951 |
Medical Image Segmentation | Kvasir-SEG | COMMA (Res2Net-50) | https://www.mdpi.com/2076-3417/12/4/2114 | mIoU | 0.852 |
Medical Image Segmentation | Kvasir-SEG | TGA-Net | https://arxiv.org/abs/2205.04280v1 | mean Dice | 0.8982 |
Medical Image Segmentation | Kvasir-SEG | TGA-Net | https://arxiv.org/abs/2205.04280v1 | mIoU | 0.8330 |
Medical Image Segmentation | Kvasir-SEG | PraNet | https://arxiv.org/abs/2006.11392v4 | Average MAE | 0.030 |
Medical Image Segmentation | Kvasir-SEG | PraNet | https://arxiv.org/abs/2006.11392v4 | mean Dice | 0.898 |
Medical Image Segmentation | Kvasir-SEG | PraNet | https://arxiv.org/abs/2006.11392v4 | S-Measure | 0.915 |
Medical Image Segmentation | Kvasir-SEG | PraNet | https://arxiv.org/abs/2006.11392v4 | max E-Measure | 0.948 |
Medical Image Segmentation | Kvasir-SEG | PraNet | https://arxiv.org/abs/2006.11392v4 | mIoU | 0.849 |
Medical Image Segmentation | Kvasir-SEG | TransResU-Net | https://arxiv.org/abs/2206.08985v1 | mean Dice | 0.8884 |
Medical Image Segmentation | Kvasir-SEG | TransResU-Net | https://arxiv.org/abs/2206.08985v1 | mIoU | 0.8214 |
Medical Image Segmentation | Kvasir-SEG | TransResU-Net | https://arxiv.org/abs/2206.08985v1 | FPS | 48.61 |
Medical Image Segmentation | Kvasir-SEG | PEFNet | https://arxiv.org/abs/2301.06673v2 | mean Dice | 0.8818 |
Medical Image Segmentation | Kvasir-SEG | PEFNet | https://arxiv.org/abs/2301.06673v2 | mIoU | 0.8163 |
Medical Image Segmentation | Kvasir-SEG | FANet | https://arxiv.org/abs/2103.17235v3 | Average MAE | 0.8153 |
Medical Image Segmentation | Kvasir-SEG | FANet | https://arxiv.org/abs/2103.17235v3 | mean Dice | 0.8803 |
Medical Image Segmentation | Kvasir-SEG | TransNetR | https://arxiv.org/abs/2303.07428v1 | mean Dice | 0.8706 |
Medical Image Segmentation | Kvasir-SEG | TransNetR | https://arxiv.org/abs/2303.07428v1 | mIoU | 0.8016 |
Medical Image Segmentation | Kvasir-SEG | TransNetR | https://arxiv.org/abs/2303.07428v1 | FPS | 54.60 |
Medical Image Segmentation | Kvasir-SEG | Yolo-SAM 2 | https://arxiv.org/abs/2409.09484v1 | mean Dice | 0.866 |
Medical Image Segmentation | Kvasir-SEG | Yolo-SAM 2 | https://arxiv.org/abs/2409.09484v1 | mIoU | 0.764 |
Medical Image Segmentation | Kvasir-SEG | DDANet | https://arxiv.org/abs/2012.15245v1 | mean Dice | 0.8576 |
Medical Image Segmentation | Kvasir-SEG | DDANet | https://arxiv.org/abs/2012.15245v1 | mIoU | 0.7800 |
Medical Image Segmentation | Kvasir-SEG | DDANet | https://arxiv.org/abs/2012.15245v1 | FPS | 69.59 |
Medical Image Segmentation | Kvasir-SEG | DoubleUnet-DCA | https://arxiv.org/abs/2303.17696v1 | mean Dice | 0.8516 |
Medical Image Segmentation | Kvasir-SEG | DoubleUnet-DCA | https://arxiv.org/abs/2303.17696v1 | mIoU | 0.7434 |
Medical Image Segmentation | Kvasir-SEG | ResUNet++ + TTA + CRF | https://arxiv.org/abs/2107.12435v1 | mean Dice | 0.8508 |
Medical Image Segmentation | Kvasir-SEG | ResUNet++ + TTA + CRF | https://arxiv.org/abs/2107.12435v1 | mIoU | 0.7800 |
Medical Image Segmentation | Kvasir-SEG | ResUNet++ + TTA + CRF | https://arxiv.org/abs/2107.12435v1 | FPS | 69.59 |
Medical Image Segmentation | Kvasir-SEG | U-Net++ | http://arxiv.org/abs/1807.10165v1 | Average MAE | 0.048 |
Medical Image Segmentation | Kvasir-SEG | U-Net++ | http://arxiv.org/abs/1807.10165v1 | mean Dice | 0.8210 |
Medical Image Segmentation | Kvasir-SEG | U-Net++ | http://arxiv.org/abs/1807.10165v1 | S-Measure | 0.862 |
Medical Image Segmentation | Kvasir-SEG | U-Net++ | http://arxiv.org/abs/1807.10165v1 | max E-Measure | 0.910 |
Medical Image Segmentation | Kvasir-SEG | ColonSegNet | https://arxiv.org/abs/2011.07631v2 | mean Dice | 0.8206 |
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